Screening Biomarkers for Nerve Injury Using Weighted Gene Co-Expression Network Analysis and Machine Learning.

ABSTRACT:

Background

Nerve injury triggers complex molecular responses involving immune activation and neuronal damage, yet the key regulatory genes and their mechanisms remain poorly understood. Here, we integrated multi-transcriptomic datasets and machine learning to identify and validate novel biomarkers of nerve injury and elucidate their functional roles.

Methods

The RNA-seq data and the single-cell transcriptome data of nerve injury and sham-surgery samples were sourced from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and three machine learning algorithms were used to identify hub genes associated with nerve injury. The expression patterns and diagnostic value of these hub genes were validate

SUBMITTER: Cao S 

PROVIDER: S-EPMC12931491 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature